code: def arrays_OPFI_BI_lengths(max_B=5, max_P=3, max_F=5, max_I=8):
      shapes = tuples(
          lengths(hi=max_B),
          lengths(hi=max_P),
          lengths(hi=max_F),
          lengths(hi=max_I),
          arrays(
              "int32",
              shape=(5,),
              elements=integers(min_value=1, max_value=10),
          ),
      )
  
      strat = shapes.flatmap(
          lambda opfi_lengths: tuples(
              ndarrays_of_shape(opfi_lengths[:-1]),
              ndarrays_of_shape(
                  (sum(opfi_lengths[-1]), opfi_lengths[-2])
              ),
              just(opfi_lengths[-1]),
          )
      )
      return strat
  
file_path: thinc/thinc/tests/strategies.py
imports: import numpy
  from hypothesis.extra.numpy import arrays
  from hypothesis.strategies import floats, integers, just, tuples
  from thinc.api import Linear, NumpyOps
label_desc: Use image node_id 14 for calling a global function with example usage: arrays_OPFI_BI_lengths(max_B, max_P, max_F, max_I) and returns: strat